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import gradio as gr
from huggingface_hub import InferenceClient
from app.services.embedding_service import EmbeddingService
from app.config import EMBEDDING_MODEL # Import from config
from app.services.preprocessor import TextPreprocessor
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Initialize EmbeddingService
embedding_service = EmbeddingService(model_name=EMBEDDING_MODEL, preprocessor=TextPreprocessor())
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
def get_embedding(text: str) -> list[float]:
"""
Endpoint to get the embedding of a text.
"""
try:
return embedding_service.get_embedding(text)
except ValueError as e:
# Handle the case where the input text is too long
return f"Error: {str(e)}"
except Exception as e:
return f"Error: {str(e)}"
# Create a separate Gradio interface for the embedding endpoint
embedding_iface = gr.Interface(
fn=get_embedding,
inputs=gr.Textbox(placeholder="Enter text here...", label="Input Text"),
outputs=gr.JSON(label="Embedding"), # Use JSON output for the embedding vector
title="Embedding Service",
description="Get the embedding of a text using the Vietnamese Bi-Encoder.",
)
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
# Combine the interfaces
demo = gr.TabbedInterface([demo, embedding_iface], ["Chatbot", "Embedding"])
if __name__ == "__main__":
demo.launch()
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